All of the TS approaches mentioned above make use of a set of hand-crafted transformation rules to decompose complex sentences into a sequence of structurally simplified components, requiring a complex rule engineering process. To overcome this expensive manual effort, Narayan2017 presented a first attempt at modelling a data-driven sentence splitting approach where simplification rewrites are learned automatically from examples of aligned complex source and simplified target sentences.
Since previously compiled TS corpora (PWKP [1]}, EW-SEW [2]}, and Newsela [3]}) contain only a small number of split examples, they are ill-suited for learning to decompose sentences into shorter, syntactically simplified components. Therefore, Narayan2017 gathered a new dataset, WebSplit, which is the first TS corpus that explicitly addresses the task of sentence splitting, while abstracting away from deletion-based and lexical
simplification operations. It is composed of over one million tuples that map a single complex sentence to a sequence of structurally simplified sentences.
